Alternatives
Products that do what Burla – Distributed computing framework for AI agents does
Hi HN! Would love any thoughts on this, we built Burla to enable anyone, even total beginners to scale Python to thousands of computers in their cloud with zero hassle. In a world of coding agents, this means something different than it used to, specifically that Burla requires almost no cloud permissions to get started. Anyone who has permission to boot a VM in their cloud can simply pip install burla and scale Python to 1000's of VM's. Burla uses your local aws cli credentials to boot VM's and the dashboard runs locally for you to monitor resources and jobs. All together this creates a UX…
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Skip the setup and run OpenClaw & Hermes, fully managed
17d ago · cloudways.com
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Hi HN folks, I have been building AI agents for quite some time now. The shift has gone from LLM + Tools → LLM Workflows → Agent + Tools + Memory, and now we are finally seeing true agency emerge: agents as systems composed of tools, command-line access, fine-grained system capabilities, and memory. This way of building agents is powerful, and I believe it is here to stay. But the real question is: are the systems powering these agents ready for that future? I do not think so. Using Docker for a single agent is not going to scale well, because agents need to be lightweight and fast. LLMs…
Mar 2026 · github.com
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CloudBot gives you a fully configured AI employee with its own cloud computer in one click. Built on OpenClaw. What you get: - Full Ubuntu desktop environment in the cloud - Pre-installed AI agent that sees the screen and controls the computer - 24/7 availability - your AI keeps working while you sleep - Uses your own API keys for AI models - Starting at $69/month The AI can use VS Code, browse the web, run terminal commands, manage files - anything you'd do on a real desktop. I wake up to completed code reviews, finished research reports, and updated documentation. Built this…
Feb 2026 · cloudbot-ai.com
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Hi HN, I’m the creator of Cordum. I’ve been working in DevOps and infrastructure for years (currently in the fintech/security space), and as I started playing with AI agents, I noticed a scary pattern. Most "safety" mechanisms rely on system prompts ("Please don't do X") or flimsy Python logic inside the agent itself. If we treat agents as autonomous employees, giving them root access and hoping they listen to instructions felt insane to me. I wanted a way to enforce hard constraints that the LLM cannot override, no matter how "jailbroken" it gets. So I built Cordum. It’s an open-source…
Jan 2026 · github.com
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Hey HN, wanted to share this cli and skill I built @ Steel (I'm the founder) I was trying to set up openclaw on railway and quickly bumped up against issues with a lack of browser access, a core component of the system. Agents like OpenClaw and CC are super good at using browsers but, similar to code sandboxes, they should be able to control these browser in the background, in parallel, without getting blocked by captchas. So I reworked agent-browser, the most popular cli for browser agents, to navigate Steel cloud browser sessions to they can run at scale and unhindered. It's a relatively…
Mar 2026 · twitter.com
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Hey HN, I’m Chris, a solo dev in Melbourne AU. For the past month I've been spending my after work hours building AgentVisa. I'm both excited (and admittedly nervous) to be sharing it with you all today. I've been spending a lot of time thinking about the future of AI agents and the more I experimented, the more I realized I was building on a fragile foundation. How do we build trust into these systems? How do we know what our agents are doing, and who gave them permission? My long-term vision is to give developers an "Agent Atlas" - a clear map of their agentic workforce, showing where…
2025 · agentvisa.dev
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Hi HN. I'm the founder of Phoenix Labs (ex TikTok, Applied AI) and we're open sourcing our internal tooling today which is like a toolchain / meta-harness for CLI agents useful for really scaling eng and creative work. We are a very small team who's building a very ambitious product so we had to find ways to squeeze every ounce of efficiency that we could get our hands on. Harness strengths of different models (Claude, GPTs) and CLI-harnesses (Claude Code, Codex), safe/robust browser integration to speed up UX/QA testing, teams cli to speed up security reviews and parallelize…
May 2026 · agents-cli.sh
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Hey HN! We've just open-sourced Agent, our framework for running computer-use workflows across multiple apps in isolated macOS/Linux sandboxes. After launching Computer a few weeks ago, we realized many of you wanted to run complex workflows that span multiple applications. Agent builds on Computer to make this possible. It works with local Ollama models (if you're privacy-minded) or cloud providers like OpenAI, Anthropic, and others. Why we built this: We kept hitting the same problems when building multi-app AI agents - they'd break in unpredictable ways, work inconsistently across…
2025 · github.com
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I built Hermes, an open-source Python framework for multi-agent financial research. Most AI “equity research” demos stop at generating text. In practice, real workflows require pulling structured XBRL financials from SEC filings, extracting labeled sections like MD&A and Risk Factors, merging macro and market data, building actual Excel models with formulas, and generating investment memos in Word or PDF. Hermes is designed to handle that full pipeline end to end. It includes 35 financial data tools covering SEC EDGAR (via edgartools), FRED, Yahoo Finance market data, and RSS-based financial…
Feb 2026 · github.com
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Hey HN, I built SuperHQ, an app that lets you run coding agents in local sandboxes (powered by Shuru). No custom UI wrapping the agents, they run as CLI/TUI like they were designed to. It just provides you the tools most of us (okay, maybe just me?) needed for running multiple coding agents in parallel without worrying about breaking your system or work environment. Each agent runs in its own microVM. You mount your projects in, writes go to a tmpfs overlay so your host is never touched, and you get a unified diff view to accept or discard changes. API keys never enter the sandbox, they…
Apr 2026 · superhq.ai
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Hello all, I'm a software developer. Over the last few months more and more of my work has turned into using coding agents instead of typing the whole code myself. Usually a few claude sessions at once, sometimes codex, one per feature or per revealed bug. I ran them in a split terminal for a few weeks, and quickly spotted two main problems. The first is that I couldn't easily tell which agent was stuck waiting on me and which was still working, so I'd cycle through sessions and checking on them. The second one: agents sharing a single branch step on each other. Two of them could be editing…
Jul 2026 · shikigami.dev
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Hey everyone! For the past two weeks my friend and I have been heads-down building Cloi, a fully local debugging agent that runs right in your terminal. You probably know the drill—every AI coding tool asks for API keys, subscriptions, and uploads your entire codebase to the cloud. Cloi does none of that: it runs entirely on your machine, with no cloud, no API keys, no subscriptions, and zero data leaving your system. What Cloi does: - Contextual error capture: Grabs your stack trace, local files, and environment to understand the issue. - Local LLM inference: Spins up Ollama on your box and…
2025 · github.com
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There's been some interesting work lately with BrowserAI (runs LLMs in the browser using WebGPU) enabling local, private AI processing. Now, the team has released BrowserAgent - a no-code tool built on top of it. BrowserAgent lets you create custom AI workflows using a drag-and-drop interface, all within your browser. This means personalized web summarizers, research assistants, or content generators can all run locally with no cloud costs and full data privacy. Check it out here - https://browseragent.dev Key features include: - No-Code Workflow Builder: Design custom AI agents…
2025 · browseragent.dev
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Built this because I was tired of every AI tool shipping my data to someone else server n0x runs the full stack LLM inference via WebGPU, autonomous ReAct agents, RAG over your own docs, sandboxed Python execution via Pyodide all inside a single browser tab. No account No keys No backend Models download once, cache in IndexedDB permanently. Biggest challenge was context window budgeting for the agent loop and making the WASM vector search non-blocking. Happy to talk architecture. GitHub: https://github.com/ixchio/n0x | Live demo: https://n0x-three.vercel.app
Mar 2026 · n0xth.vercel.app
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I've been running Claude agents for various automation tasks — monitoring crypto news, syncing Todoist, running health checks — and I kept hitting the same problem: there's no clean way to deploy an agent that just runs on a schedule without a human babysitting it. Every agent framework I looked at was built around chat interfaces or one-shot workflows. I wanted something closer to cron for AI agents — define a task, give it a schedule, let it run forever. So I built Ductwork. You define tasks as simple JSON files — a prompt, a schedule, optional memory and skills — and ductwork handles…
Mar 2026 · github.com
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Hey HN. I built an AI agent harness over the past few months and I'm open sourcing it today. Some context on why. I've been building with Claude Code daily using this harness. It orchestrates multiple AI agents as a team, with a dashboard, chat, kanban board, the works. I used it to build a full SaaS product (MyUpMonitor, https://myupmonitor.com) in about 24 hours of focused coding. Then yesterday Anthropic announced Mythos and decided to keep it behind closed doors. Meanwhile I'm paying for Claude and I can't access their best model. I don't think that is nice at all... So I'm…
Apr 2026 · github.com
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Hey HN, Over the past 10 months I've been using Claude Code heavily, and one limitation kept coming up: you can really only run one coding agent at a time. While one agent is refactoring something, the rest of the repo is basically blocked unless you start manually juggling branches and working directories. The core issue is that AI coding agents operate directly in your filesystem. If two agents run in the same working directory they quickly start stepping on each other’s changes. Git worktrees turned out to be a surprisingly good primitive for solving this. So I built ChatML, a Desktop app…
Mar 2026 · github.com
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